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About This Role
What You’ll Do:
- Design, build, and maintain scalable automation solutions to support analytics and reporting platforms
- Partner with internal and client\-facing stakeholders to identify, streamline, and eliminate manual or inefficient processes
- Develop and support backend and frontend automations using modern programming languages, frameworks, and APIs
- Create and optimize data assets including tables, scripts, datasets, dashboards, and reporting tools to meet evolving business needs
- Leverage analytical and automation platforms such as Power BI, Alteryx, KNIME, and similar tools to deliver insights and enable self\-service analytics
- Optimize and maintain SQL databases across cloud and on ‑ prem environments , focusing on performance, reliability, and scalability
- Measure, track, and communicate automation value by quantifying ROI, efficiency gains, and risk reduction
- Support project ‑ based automation initiatives , contribute to solution design , and mentor junior team members on tools, best practices, and standards
Acosta is a part of Acosta Group – a collective of the industry’s most trusted retail, marketing and foodservice agencies reimagining the way people connect with brands at every point in their shopping journey.
Specializing in retail sales services, digital strategy, and business intelligence, Acosta empowers brands to thrive in the world of omnichannel shopping. Our sales and digital teams build lasting relationships, ensuring our client brands get the space they deserve in stores and outperform the competition online, while our merch reps make brands shine in retail locations across the world.
But it’s not just about what we do – it’s about who we are. With a team of over 20,000 associates, we’re a community of forward\-thinking, value\-driven professionals committed to an unmatched level of trust and transparency in the industry. And, we understand the importance of work\-life balance, which is why many of our field roles provide our associates with flexible scheduling options. Join us and be a part of a team that values growth and making a real impact for our clients, retail partners and their customers.
Acosta Group is an equal opportunity employer and will ensure that applicants with disabilities are provided with reasonable accommodations. If reasonable accommodation is needed, please contact [email protected] . Be sure to include "Applicant Accommodation" in the subject of your email to expedite the request.
Acosta Group believes in good faith that the minimum and maximum annual salary or hourly compensation range for this opportunity is accurate and reasonable at the time of posting.
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\#DiscoverYourPath
Automation Developer to support enterprise analytics and business intelligence teams by leveraging tools and custom automation solutions to deliver scalable, insight driven results.
Why Join Us:
- Work on high\-impact automation projects that support analytics and business intelligence
- Collaborate with cross\-functional teams across technology and client\-facing groups
- Opportunity to influence scalable solutions and mentor others
\#DiscoverYourPath
Why Join Us:
- Work on high\-impact automation projects that support analytics and business intelligence
- Collaborate with cross\-functional teams across technology and client\-facing groups
- Opportunity to influence scalable solutions and mentor others
\#DiscoverYourPath
What You Bring:
- Bachelor’s degree (BA or BS)
- 5\+ years of advanced programming experience (Python, C\#, Java, .NET, or similar)
- Strong understanding of software architecture and automation design
- Advanced SQL and database optimization experience
- Proven problem\-solving, analytical , and project management skills
- Excellent communication skills and ability to work independently .
What You Bring:
- Bachelor’s degree (BA or BS)
- 5\+ years of advanced programming experience (Python, C\#, Java, .NET, or similar)
- Strong understanding of software architecture and automation design
- Advanced SQL and database optimization experience
- Proven problem\-solving, analytical , and project management skills
- Excellent communication skills and ability to work independently .
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Acosta Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Acosta Group AI Hiring
Acosta Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Jacksonville, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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